Roberto A. Ribas Zunino

Roberto A. Ribas Zunino / Research

Research interests

One thread runs through everything I've done: measure metabolism where it happens, then use that to change an outcome. These are the problems I want to spend the next decade on. Some build directly on my work; others are long shots, and that is the point. The ideas most likely to help the most people are often the ones that look unlikely at first.

Builds on my work

Immune-mediated disease: Crohn’s, IBD, uveitis and Graves’

This one is personal: I live with Crohn's disease, and I want to help find better treatments for everyone who shares it.

Crohn's disease and ulcerative colitis, uveitis, and Graves' disease look like different diseases of different organs, but they share a core problem: an immune response that should switch off stays on and damages the body's own tissue. In inflammatory bowel disease it misfires against the gut, where it meets trillions of microbes and everything we eat; in high-income countries most uveitis is immune-mediated inflammation inside the eye; and Graves' disease, the most common cause of hyperthyroidism, is driven by antibodies that overstimulate the thyroid.

Immune cells rewire their metabolism when they switch on, and those metabolic states help decide whether inflammation resolves or becomes chronic. That field, immunometabolism, is where my metabolism work meets immunology. Inflamed tissue is a patchwork: immune cells, epithelium, and bacteria sit side by side, each running its own metabolism, and those differences help decide who flares, who heals, and who responds to treatment. I want to map what immune cells are doing metabolically in place, in inflamed and healing tissue, starting with the gut, and find the states that drive disease and could be reversed.

Glycans are a natural bridge from my work. The gut's protective mucus is made of mucins, proteins heavily coated in glycans. I have spent years imaging glycans and glycogen in tissue, and extending that to mucin glycans is a natural next step. I've also contributed to work showing that glycan metabolism can drive a disease (Alzheimer's, below), and FUT2, which encodes an enzyme that adds fucose to gut glycans, is a known Crohn's disease risk gene. As an undergraduate I applied AI to combine host and microbiome data for disease prediction. Joining those two, spatial measurement and AI on the host–microbe interface, is where I want to start. From the gut, the same questions carry to the eye, the thyroid, and other organs where the immune system turns on the body's own tissue.

Builds on my work

Cancer: metabolism in place

A tumor or a brain region is not one metabolic state. Cells next to each other can run on different fuels depending on their lineage, their neighbors, and their oxygen supply. Two studies I contributed to showed how much a single fuel can matter. In lung adenocarcinoma, spatial screens of human tumors linked glycogen build-up to higher grade and worse survival, and raising glycogen in mice sped tumors up while removing glycogen synthase held them back (Nature Metabolism, 2025). In the deadliest, MYC-driven subtype of medulloblastoma, tumors stored fat in lipid droplets, and switching off DGAT1, the enzyme that builds that store, extended survival in mice carrying human MYC-high tumors (Cancer Cell, 2026). Next I want to read lineage, location, and metabolism in the same tissue, so we can see which cells carry these dependencies and target them.

Builds on my work

Alzheimer’s disease and the injured, aging brain

Metabolism goes wrong in Alzheimer's disease and after brain injury, and in both cases it may be something we can act on. I contributed to work showing that excess glycan building, hyperglycosylation, is a driver of Alzheimer's: in Alzheimer's model mice, dialing down glycan-making enzymes improved memory and glucosamine supplements made it worse, and in health records glucosamine use was associated with higher mortality and more progression from mild cognitive impairment to dementia (Nature Metabolism, 2026). I also contributed to a study showing that parvalbumin interneurons, thought to be among the most energy-demanding cells in the brain, lose their metabolic specializations in a rodent model of traumatic brain injury, with matching changes in published human brain tissue data (Nature Neuroscience, in press). I want to understand which of these changes are causes, and which can be treated.

Long shot

Measurement that scales

Spatial omics today is slow, expensive, and lives in a few specialized centers. Having helped run one of those centers, I know where the bottlenecks are: sample preparation, instrument time, and analysis. I want to build tools, including AI, that make these measurements fast and cheap enough for clinical and population-scale use. MetaVision3D, which automates turning serial sections into a 3D map, was a first step in automating the analysis.

Long shot

Biology at the extremes

Some animals survive freezing, starvation, or months without oxygen by switching their metabolism into states we barely understand. Hibernators drop their body temperature and energy use for weeks, then come back unharmed. If we understood how, we could protect organs during transplant and stroke, slow tissue damage, and maybe slow aging itself. Metabolism is the common language of all of these states, and it is the thing I know how to measure.

What I’ve built so far

Long shots only read as credible next to proof that I deliver. These are the case studies behind them:

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